Tag Archives: affective search

I am in the last stages of my thesis and it feels like a dream come true! It is an extraordinary experience when you approach the finish line, finalizing a six-year doctoral research. My most recent speaking experience was at the TTI Vanguard Conference at the Ritz-Carlton in December 2017. As I am concluding the first draft thesis, I thought of sharing the Abstract here with you. Happy New Year!

ABSTRACTThe affective and neurological components of information retrieval system design have increasingly become an essential part of research in human-information interaction and interactive information retrieval. These sophisticated processes are composed of not only human cognitive processes but also emotional and neuropsychological (NP) responses. One of the most cited information search process models, the Information Search Process (ISP) model (Kuhlthau, 1991), identified three realms of user experience; affective (feelings), cognitive (thoughts), and physical (actions) realms. While the ISP model identified three dimensions of user experience, it does not include the NP dimension of the brain. Neither does it examine the impact, if any, of emotional states on the NP dimension.

This research contributes three original findings to the field of Information Science, positioned in Neuro Information Science. First, this experimental research discovered and mapped the neurophysiological and the emotional dimensions of information search processes. Second, this thesis connected the dots between the discrete emotions of Kuhlthau’s model (1991), the continuous dimensions of emotion scale of Scherer (2005), and the neurophysiological and emotional aspects of information search processes (Sarraf, 2018). Third, this research contributed to the body of knowledge for detection of dimensions of emotions using EEG devices. 48 participants performed search tasks during neutral, positive, and negative emotional states. This study collected brain frequencies through the Emotiv EEG neuroheadset. The results indicated that there were clear differences in the brain frequencies within different locations of the brain, depending on the ISP stage and the emotional state.

One of the major findings of this study discovered that, regardless of the information search stages and/or emotional states, the dominant active part of the brain was the upper left brain, which primarily handles logical and analytical thoughts. Moreover, this study showed that when investigating (exploration stage) and forming focus (formulation stage) in searching for information, the brain was extremely active, thinking logical/analytical thoughts. But the brain slowed down in logical/analytical thinking when gathering for information (collection stage).

On the other hand, positive feelings harmonized the neural activities of the brain regardless of the stages of information search. During information search stages, the brain activities balanced out, thinking only logical analytical thoughts. Yet negativity affected the brain drastically in that, when investigating information, while the logical/analytical thoughts increased, so did intuition and interpersonal feelings.

This study also connected the discrete emotions and the continuous emotions on the valence-arousal scale. The continuous emotions roughly changed from (a) negative-excited to (b) positive-calm to (c) positive-excited. This study suggest that, the corresponding neurophysiological aspects of the ISP stages change from (a) gamma to (b) gamma to (c) beta in the upper left brain, which handles logical and analytical thinking.

Lastly, this study supported the existing experimental research methods and results when detecting dimensions of emotions using EEG devices. During positive emotional states, beta waves in the upper left brain were the most dominant. During negative emotions, beta and gamma waves were dominant both in the upper left and in the right brain hemisphere.The right brain hemisphere was active with beta and gamma waves when feeling negative emotions and in positive emotions the brain was active in the upper left quadrant eliciting beta waves.

In an era where we are creating brain controlled airplanes, neurogaming, and robots that learn behavior by reading human emotions, there appear to be no limits in having search engines read human emotions in order to improve search results based on the neurological feedback they receive from user’s brain waves. Thanks to companies such as Interaxon and Emotiv, EEG devices have readily been made available to researchers interested in neuro-related studies, who otherwise would have not had access to expensive fMRI machines. Although the two devices measure different entities of the brain, nonetheless, EEG devices help enthusiastic, but low budget, researchers (such as me!) conduct neuro-related studies.

My doctoral research topic aims to examine cognitive relationships between dimensions of human emotions and information retrieval, as in search performance, in the field of neuro information science (Gwizdka, 2012). This study aims to increase our understanding in regards to affective search, improving information systems design practices, and investigating ways to design ‘smart’ information systems that learn and improve search results based on neuro feedback.

To illustrate, emerging expressions, such as “pleasurable engineering” or “emotional design”, have not only become the driving factors in information retrieval system design (Nahl & Bilal, 2007) but also illustrate the important role of emotions in human-computer-interaction. Information retrieval entails complicated cognitive processes, composed of human cognitive processes as well as human physiological and neurological reactions (Picard, 2001). However, our understanding of how emotions affect information retrieval is limited (Nahl & Bilal, 2007), so is our understanding when it comes to the effects of physiological and neurological responses on information retrieval, more specifically on web search performance.

Hence, for us to be able to design better search engines, we need to understand both ‘human-computer-interaction’ as well as ‘brain-computer-interaction’ processes, such that the two not be treated separately.